2025
Detection of emergency department patients at risk of dementia through artificial intelligence
Cohen I, Taylor R, Xue H, Faustino I, Festa N, Brandt C, Gao E, Han L, Khasnavis S, Lai J, Mecca A, Sapre A, Young J, Zanchelli M, Hwang U. Detection of emergency department patients at risk of dementia through artificial intelligence. Alzheimer's & Dementia 2025, 21: e70334. PMID: 40457744, PMCID: PMC12130574, DOI: 10.1002/alz.70334.Peer-Reviewed Original ResearchConceptsElectronic health record dataHealth record dataEmergency departmentDetect dementiaDementia detectionYale New Haven HealthRecord dataRisk of dementiaEmergency department patientsBalance detection accuracyDementia algorithmsImprove patient outcomesCare coordinationCare transitionsDementia diagnosisReal-time applicationsClinical decision-makingClinician supportED usePatient safetyProbable dementiaMachine learning algorithmsED workflowED visitsED encountersUsing natural language processing to identify emergency department patients with incidental lung nodules requiring follow‐up
Moore C, Socrates V, Hesami M, Denkewicz R, Cavallo J, Venkatesh A, Taylor R. Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow‐up. Academic Emergency Medicine 2025, 32: 274-283. PMID: 39821298, DOI: 10.1111/acem.15080.Peer-Reviewed Original ResearchNatural language processingIncidental lung nodulesFollow-upChest CTsCT reportsF1 scoreLung nodulesEmergency departmentLanguage processingFollow-up of incidental findingsIncidental findingNatural language processing developersAbsence of malignancyMetrics of precisionNatural language processing pipelineNatural language processing metricsChest CT reportsRecommended follow-upEmergency department patientsFollow-up rateLanguage modelLung cancerReduce errorsMalignancyDepartment patients
2020
Patient factors associated with SARS‐CoV‐2 in an admitted emergency department population
Haimovich A, Warner F, Young HP, Ravindra NG, Sehanobish A, Gong G, Wilson FP, van Dijk D, Schulz W, Taylor R. Patient factors associated with SARS‐CoV‐2 in an admitted emergency department population. Journal Of The American College Of Emergency Physicians Open 2020, 1: 569-577. PMID: 32838371, PMCID: PMC7280703, DOI: 10.1002/emp2.12145.Peer-Reviewed Original ResearchCOVID-19 positive patientsPatient characteristicsPatient factorsCOVID-19Positive COVID-19 testMultivariable logistic regression modelLow pulse oximetryChronic lung diseaseRetrospective observational studyCOVID-19 test resultsEmergency department patientsPrimary outcome measureEmergency department populationLower leukocyte countsPositive COVID-19 resultHistory of alcoholPositive test resultsSARS-CoV-2 virusSARS-CoV-2COVID-19 testingLogistic regression modelsCOVID-19 testCOVID-19 resultsED cohortNegative patients
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